Technological advances have lead to process-driven automated equipment that is increasingly complex. A tool system to accomplish a specific goal or perform a specific, highly technical process can commonly incorporate multiple functional elements to reach the goal or successfully execute the process, and various sensors that collect data to monitor the operation of the equipment. Such automated equipment can generate a large volume of data. Data can include information related to a product or a service performed as a part of the specific task and/or sizable log information related to the process. For example, process data and/or metrology data can be collected during a manufacturing process and/or stored in one or more datasets.
While modern electronic storage technologies can afford retaining constantly increasing quantities of data, utilization of the accumulated data remains far from optimal. Examination and interpretation of collected information generally requires human intervention. For example, a process on a semiconductor manufacturing tool may run for seventeen hours (e.g., 61,200 seconds). During processing, the semiconductor manufacturing tool may output sensor measurements every second via, for example, several hundred sensors. Accordingly, large datasets that include the output sensor measurements must then be manually studied (e.g., by process engineers) during process development and/or troubleshooting activities. However, it is difficult to determine emerging tool failures related to a tool system (e.g., based on the large datasets that include the output sensor measurements) using human intervention.
The above-described deficiencies of today's fabrication systems are merely intended to provide an overview of some of the problems of conventional systems, and are not intended to be exhaustive. Other problems with conventional systems and corresponding benefits of the various non-limiting embodiments described herein may become further apparent upon review of the following description.